The Treatment Section has been engaged in a long-term project to improve treatment for substance dependence through behavioral, pharmacologic, or combined behavioral and pharmacologic interventions. Substantial effort has been directed toward identifying the most effective ways to deliver contingency management, a behavioral treatment in which incentives are used to increase the frequency of desirable behaviors, such as drug abstinence or medication adherence. A major challenge associated with contingency management is its likely prohibitive cost and staff/resource intensity, especially when the reinforcement follows an escalating schedule (i.e., when each consecutive occurrence of the desired behavior is reinforced more than the lastan especially effective treatment). One of the major implementation challenges for community clinics is keeping track of each patients prior earnings so that current earnings can be calculated on the spot. To address this and related challenges, in collaboration with the Biomedical Informatics Section of the NIDA IRP, we developed a software application, the Automated Contingency Management (ACM) decision support system for abstinence reinforcement. We are continuing to improve the system and to develop mechanisms to make it available to community treatment programs at no cost to them. We are completing a project field-testing the usability and robustness of the current version of the software, Motivational Incentives Implementation Software (MIIS), implemented under conditions simulating those of a community treatment program little technology infrastructure or staff expertise. This system will be distributed to community treatment programs to promote technology transfer and increase community use of evidence-based treatments for addiction. In further technology-development work, we are exploring the use of handheld electronic devices for treatment delivery in patients daily environments. We have completed a pilot study using these devices to remind patients to complete homework assigned by counselors as part of Cognitive-Behavioral Therapy (CBT). We conducted a within-subjects 2x2 randomized-block-design crossover study to investigate the effects of homework-task difficulty and electronic-diary reminders on compliance to and quality of homework completion among methadone-maintained cocaine and heroin users in CBT. Participants, in addition to completing a homework task between each of 12 weekly therapy sessions, carried a personal digital assistant (PDA), which was programmed to provide a daily reminder regarding homework completion during alternate weeks of the study. Homework was given in two different forms during alternating 3-week blocks: standard sheets of text versus simplified, illustrated booklets. Neither the simplified homework nor the PDA reminders significantly increased homework completion rates. For homework simplification, there were (non-significant) trends toward beneficial effects on homework completion;however, electronic prompting as implemented in this study had (non-significant) deleterious effects on homework completion. There were trends for the simplified homework tasks to be rated higher by counselors in terms of participants enthusiasm and understanding, although, unexpectedly, electronically prompted homework was also rated higher in terms of participants understanding. We suspect that a mobile intervention for this population may need to go beyond a reminder beep and incorporate interactive elements;we are beginning to pursue this line of intervention. In related work, we are developing Geographical Momentary Assessment, a descriptive approach to better measure and understand the relationships among mood, drug use, and environmental exposure to psychosocial stressors. We remain committed to transforming description into intervention. For example, we have shown that electronic-diary studies can provide amazing insight into the daily lives of substance abusers during treatment and data that are sensitive to behavioral changes during even brief periods of abstinence. The technologies that enable us to collect data on drug use, craving, and stress in the field may also be used for delivery of treatment in the field, perhaps in response to the patients own self-reported behaviors or previously identified triggers. The Treatment Section continues to investigate predictors that might be useful for patient-treatment matching to improve the delivery of appropriate treatments to individual patients. We recently undertook a secondary analysis of data from one of our outpatient clinical trials of buprenorphine treatment for concurrent cocaine and opioid dependence (13 weeks, N = 200). We evaluated the association between cigarette smoking (lifetime cigarette smoking status, number of cigarettes smoked per day prior to study entry) and short-term treatment outcome (% of urine samples positive for cocaine or opioids, treatment retention). Nicotine-dependent smokers (66% of participants) had a significantly higher percentage of cocaine-positive urine samples than non-smokers (12% of participants) (76% vs. 62%), but did not differ in percentage of opioid-positive urine samples or treatment retention. Number of cigarettes smoked per day at baseline was positively associated with percentage of cocaine-positive urine samples, even after controlling for baseline sociodemographic and drug-use characteristics, but was not significantly associated with percentage of opioid-positive urine samples or treatment retention. These results suggest that cigarette smoking is associated with poorer short-term outcome in outpatient treatment for cocaine dependence, but perhaps not for concurrent opioid dependence, and support the importance of offering smoking-cessation treatment to cocaine-dependent patients.